Institute of Fundamental Technological Research

Objective: Neoadjuvant chemotherapy (NAC) is widely used in the treatment of breast cancer. However, to date, there are no fully reliable, non-invasive methods for monitoring NAC. In this article, we propose a new method for classifying NAC-responsive and unresponsive tumors using quantitative ultrasound. Methods: The study used ultrasound data collected from breast tumors treated with NAC. The proposed method is based on the hypothesis that areas that characterize the effect of therapy particularly well can be found. For this purpose, parametric images of texture features calculated from tumor images were converted into NAC response probability maps, and areas with a probability above 0.5 were used for classification. Results: The results obtained after the third cycle of NAC show that the classification of tumors using the traditional method (AUC = 0.81 - 0.88) can be significantly improved thanks to the proposed new approach (AUC = 0.84–0.94). This improvement is achieved over a wide range of cutoff values (0.2-0.7), and the probability maps obtained from different quantitative parameters correlate well. Conclusion: The results suggest that there are tumor areas that are particularly well suited to assessing response to NAC. Significance: The proposed approach to monitoring the effects of NAC not only leads to a better classification of responses, but also may contribute to a better understanding of the microstructure of neoplastic tumors observed in an ultrasound examination.

breast cancer,neoadjuvant chemotherapy,quantitative ultrasound,treatment monitoring.

Objective: Neoadjuvant chemotherapy (NAC) in breast cancer requires non-invasive methods of monitoring its effects after each dose of drug therapy. The aim is to isolate responding and non-responding tumors prior to surgery in order to increase patient safety and select the optimal medical follow-up. Methods: A new method of monitoring NAC therapy has been proposed. The method is based on image quality indexes (IQI) calculated from ultrasound data obtained from breast tumors and surrounding tissue. Four different tissue regions from the preliminary set of 38 tumors and three data pre-processing techniques are considered. Postoperative histopathology results were used as a benchmark in evaluating the effectiveness of the IQI classification. Results: Out of ten parameters considered, the best results were obtained for the Gray Relational Coefficient. Responding and non-responding tumors were predicted after the first dose of NAC with an area under the receiver operating characteristics curve of 0.88 and 0.75, respectively. When considering subsequent doses of NAC, other IQI parameters also proved usefulness in evaluating NAC therapy. Conclusions: The image quality parameters derived from the ultrasound data are well suited for assessing the effects of NAC therapy, in particular on breast tumors.

Quantitative ultrasound; Image quality; Neoadjuvant chemotherapy; Breast cancer; Treatment response

In this paper, we propose a novel deep learning method for joint classification and segmentation of breast masses based on radio-frequency (RF) ultrasound (US) data. In comparison to commonly used classification and segmentation techniques, utilizing B-mode US images, we train the network with RF data (data before envelope detection and dynamic compression), which are considered to include more information on tissue’s physical properties than standard B-mode US images. Our multi-task network, based on the Y-Net architecture, can effectively process large matrices of RF data by mixing 1D and 2D convolutional filters. We use data collected from 273 breast masses to compare the performance of networks trained with RF data and US images. The multi-task model developed based on the RF data achieved good classification performance, with area under the receiver operating characteristic curve (AUC) of 0.90. The network based on the US images achieved AUC of 0.87. In the case of the segmentation, we obtained mean Dice scores of 0.64 and 0.60 for the approaches utilizing US images and RF data, respectively. Moreover, the interpretability of the networks was studied using class activation mapping technique and by filter weights visualizations.

breast mass classification, breast mass segmentation, convolutional neural networks, deep learning, quantitative ultrasound, ultrasound imagin

Temperature monitoring in ultrasound (US) imaging is important for various medical treatments, such as high-intensity focused US (HIFU) therapy or hyperthermia. In this work, we present a deep learning based approach to temperature monitoring based on radio-frequency (RF) US data. We used Siamese neural networks in an unsupervised way to spatially compare RF data collected at different time points of the heating process. The Siamese model consisted of two identical networks initially trained on a large set of simulated RF data to assess tissue backscattering properties. To illustrate our approach, we experimented with a tissue-mimicking phantom and an ex-vivo tissue sample, which were both heated with a HIFU transducer. During the experiments, we collected RF data with a regular US scanner. To determine spatiotemporal variations in temperature distribution within the samples, we extracted small 2D patches of RF data and compared them with the Siamese network. Our method achieved good performance in determining the spatiotemporal distribution of temperature during heating. Compared with the temperature monitoring based on the change in radio-frequency signal backscattered energy parameter, our method provided more smooth spatial parametric maps and did not generate ripple artifacts. The proposed approach, when fully developed, might be used for US based temperature.

temperature monitoring, high intensity ultrasound, deep learning, transfer learning, ultrasound imaging

Purpose: Neo-adjuvant chemotherapy (NAC) is used in breast cancer before tumor surgery to reduce the size of the tumor and the risk of spreading. Monitoring the effects of NAC is important because in a number of cases the response to therapy is poor and requires a change in treatment. A new method that uses quantitative ultrasound to assess tumor response to NAC has been presented. The aim was to detect NAC unresponsive tumors at an early stage of treatment. Methods: The method assumes that ultrasound scattering is different for responsive and nonresponsive tumors. The assessment of the NAC effects was based on the differences between the histograms of the ultrasound echo amplitude recorded from the tumor after each NAC dose and from the tissue phantom, estimated using the Kolmogorov–Smirnov statistics (KSS) and the symmetrical Kullback–Leibler divergence (KLD). After therapy, tumors were resected and histopathologically evaluated. The percentage of residual malignant cells was determined and was the basis for assessing the tumor response. The data set included ultrasound data obtained from 37 tumors. The performance of the methods was assessed by means of the area under the receiver operating characteristic curve (AUC). Results: For responding tumors, a decrease in the mean KLD and KSS values was observed after subsequent doses of NAC. In nonresponding tumors, the KLD was higher and did not change in subsequent NAC courses. Classification based on the KSS or KLD parameters allowed to detect tumors not respond- ing to NAC after the first dose of the drug, with AUC equal 0.83±0.06 and 0.84±0.07, respectively. After the third dose, the AUC increased to 0.90±0.05 and 0.91±0.04, respectively. Conclusions: The results indicate the potential usefulness of the proposed parameters in assessing the effectiveness of the NAC and early detection of nonresponding cases.

breast cancer, neoadjuvant therapy assessment, quantitative ultrasound

Objective. Prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer is important for patient outcomes. In this work, we propose a deep learning based approach to NAC response prediction in ultrasound (US) imaging. Approach. We develop recurrent neural networks that can process serial US imaging data to predict chemotherapy outcomes. We present models that can process either raw radio-frequency (RF) US data or regular US images. The proposed approach is evaluated based on 204 sequences of US data from 51 breast cancers. Each sequence included US data collected before the chemotherapy and after each subsequent dose, up to the 4th course. We investigate three pre-trained convolutional neural networks (CNNs) as back-bone feature extractors for the recurrent network. The CNNs were pre-trained using raw US RF data, US b-mode images and RGB images from the ImageNet dataset. The first two networks were developed using US data collected from malignant and benign breast masses. Main results. For the pre-treatment data, the better performing network, with back-bone CNN pre-trained on US images, achieved area under the receiver operating curve (AUC) of 0.81 (±0.04). Performance of the recurrent networks improved with each course of the chemotherapy. For the 4th course, the better performing model, based on the CNN pre-trained with RGB images, achieved AUC value of 0.93 (±0.03). Statistical analysis based on the DeLong test presented that there were no significant differences in AUC values between the pre-trained networks at each stage of the chemotherapy (p-values > 0.05). Significance. Our study demonstrates the feasibility of using recurrent neural networks for the NAC response prediction in breast cancer US.

breast cancer, deep learning, neoadjuvant chemotherapy, reccurent neural networks, ultrasound imaging

Aim of the study: Deep neural networks have achieved good performance in breast mass classification in ultrasound imaging. However, their usage in clinical practice is still lim¬ited due to the lack of explainability of decisions conducted by the networks. In this study, to address the explainability problem, we generated saliency maps indicating ultrasound image regions important for the network’s classification decisions. Material and methods: Ultrasound images were collected from 272 breast masses, including 123 malignant and 149 benign. Transfer learning was applied to develop a deep network for breast mass clas¬sification. Next, the class activation mapping technique was used to generate saliency maps for each image. Breast mass images were divided into three regions: the breast mass region, the peritumoral region surrounding the breast mass, and the region below the breast mass. The pointing game metric was used to quantitatively assess the overlap between the saliency maps and the three selected US image regions. Results: Deep learning classifier achieved the area under the receiver operating characteristic curve, accuracy, sensitivity, and specific¬ity of 0.887, 0.835, 0.801, and 0.868, respectively. In the case of the correctly classified test US images, analysis of the saliency maps revealed that the decisions of the network could be associated with the three selected regions in 71% of cases. Conclusions: Our study is an important step toward better understanding of deep learning models developed for breast mass diagnosis. We demonstrated that the decisions made by the network can be related to the appearance of certain tissue regions in breast mass US images.

deep learning, breast mass diagnosis, attention maps, explainability

To investigate the performance of multiparametric ultrasound for the evaluation of treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Breast cancer patients who were scheduled to undergo NAC were invited to participate in this study. Changes in tumour echogenicity, stiffness, maximum diameter, vascularity and integrated backscatter coefficient (IBC) were assessed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was considered as standard of reference. RMC < 30% was considered a good response and > 70% a poor response. The correlation coefficients of these parameters were compared with RMC from post-operative histology. Linear Discriminant Analysis (LDA), cross-validation and Receiver Operating Characteristic curve (ROC) analysis were performed. Thirty patients (mean age 56.4 year) with 42 lesions were included. There was a significant correlation between RMC and echogenicity and tumour diameter after the 3rd course of NAC and average stiffness after the 2nd course. The correlation coefficient for IBC and echogenicity calculated after the first four doses of NAC were 0.27, 0.35, 0.41 and 0.30, respectively. Multivariate analysis of the echogenicity and stiffness after the third NAC revealed a sensitivity of 82%, specificity of 90%, PPV = 75%, NPV = 93%, accuracy = 88% and AUC of 0.88 for non-responding tumours (RMC > 70%). High tumour stiffness and persistent hypoechogenicity after the third NAC course allowed to accurately predict a group of non-responding tumours. A correlation between echogenicity and IBC was demonstrated as well.

Echocardiographic assessment of systolic and diastolic function of the heart is often limited by image quality. However, the aortic root is well visualized in most patients. We hypothesize that the aortic root motion may correlate with the systolic and diastolic function of the left ventricle of the heart. Data obtained from 101 healthy volunteers (mean age 46.6 ± 12.4) was used in the study. The data contained sequences of standard two-dimensional (2D) echocardiographic B-mode (brightness mode, classical ultrasound grayscale presentation) images corresponding to single cardiac cycles. They also included sets of standard echocardiographic Doppler parameters of the left ventricular systolic and diastolic function. For each B-mode image sequence, the aortic root was tracked with use of a correlation tracking algorithm and systolic and diastolic values of traveled distances and velocities were determined. The aortic root motion parameters were correlated with the standard Doppler parameters used for the assessment of LV function. The aortic root diastolic distance (ARDD) mean value was 1.66 ± 0.26 cm and showed significant, moderate correlation (r up to 0.59, p < 0.0001) with selected left ventricular diastolic Doppler parameters. The aortic root maximal diastolic velocity (ARDV) was 10.8 ± 2.4 cm/s and also correlated (r up to 0.51, p < 0.0001) with some left ventricular diastolic Doppler parameters. The aortic root systolic distance (ARSD) was 1.63 ± 0.19 cm and showed no significant moderate correlation (all r values < 0.40). The aortic root maximal systolic velocity (ARSV) was 9.2 ± 1.6 cm/s and correlated in moderate range only with peak systolic velocity of medial mitral annulus (r = 0.44, p < 0.0001). Based on these results, we conclude, that in healthy subjects, aortic root motion parameters correlate significantly with established measurements of left ventricular function. Aortic root motion parameters can be especially useful in patients with low ultrasound image quality precluding usage of typical LV function parameters.

Early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for guiding therapy decisions. In this work, we propose a deep learning based approach for the early NAC response prediction in ultrasound (US) imaging. We used transfer learning with deep convolutional neural networks (CNNs) to develop the response prediction models. The usefulness of two transfer learning techniques was examined. First, a CNN pre-trained on the ImageNet dataset was utilized. Second, we applied double transfer learning, the CNN pre-trained on the ImageNet dataset was additionally fine-tuned with breast mass US images to differentiate malignant and benign lesions. Two prediction tasks were investigated. First, a L1 regularized logistic regression prediction model was developed based on generic neural features extracted from US images collected before the chemotherapy (a priori prediction). Second, Siamese CNNs were used to quantify differences between US images collected before the treatment and after the first and second course of NAC. The proposed methods were evaluated using US data collected from 39 tumors. The better performing deep learning models achieved areas under the receiver operating characteristic curve of 0.797 and 0.847 in the case of the a priori prediction and the Siamese model, respectively. The proposed approach was compared with a
method based on handcrafted morphological features. Our study presents the feasibility of using transfer learning with CNNs for the NAC response prediction in US imaging.

breast cancer, deep learning, neoadjuvant chemotherapy, Siamese convolutional neural networks, ultrasound imaging

The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC.

quantitative ultrasound, B-mode ultrasound, echogenicity, breast cancer, neoadjuvant chemotherapy

We propose a novel approach to breast mass classification based on deep learning models that utilize raw radio-frequency (RF) ultrasound (US) signals. US images, typically displayed by US scanners and used to develop computer-aided diagnosis systems, are reconstructed using raw RF data. However, information related to physical properties of tissues present in RF signals is partially lost due to the irreversible compression necessary to make raw data readable to the human eye. To utilize the information present in raw US data, we develop deep learning models that can automatically process small 2D patches of RF signals and their amplitude samples. We compare our approach with classification method based on the Nakagami parameter, a widely used quantitative US technique utilizing RF data amplitude samples. Our better performing deep learning model, trained using RF signals and their envelope samples, achieved good classification performance, with the area under the receiver attaining operating characteristic curve (AUC) and balanced accuracy of 0.772 and 0.710, respectively. The proposed method significantly outperformed the Nakagami parameter-based classifier, which achieved AUC and accuracy of 0.64 and 0.611, respectively. The developed deep learning models were used to generate parametric maps illustrating the level of mass malignancy. Our study presents the feasibility of using RF data for the development of deep learning breast mass classification models.

breast lesion classification, convolutional neural networks, deep learning, radio-frequency signals, ultrasound imaging

The presented studies evaluate for the first time the efficiency of tumour classification based on the quantitative analysis of ultrasound data originating from the tissue surrounding the tumour. 116 patients took part in the study after qualifying for biopsy due to suspicious breast changes. The RF signals collected from the tumour and tumour-surroundings were processed to determine quantitative measures consisting of Nakagami distribution shape parameter, entropy, and texture parameters. The utility of parameters for the classification of benign and malignant lesions was assessed in relation to the results of histopathology. The best multi-parametric classifier reached an AUC of 0.92 and of 0.83 for outer and intra-tumour data, respectively. A classifier composed of two types of parameters, parameters based on signals scattered in the tumour and in the surrounding tissue, allowed the classification of breast changes with sensitivity of 93%, specificity of 88%, and AUC of 0.94. Among the 4095 multi-parameter classifiers tested, only in eight cases the result of classification based on data from the surrounding tumour tissue was worse than when using tumour data. The presented results indicate the high usefulness of QUS analysis of echoes from the tissue surrounding the tumour in the classification of breast lesions.

Background: Neoadjuvant chemotherapy (NAC) is used in patients with breast cancer to reduce tumor focus, metastatic risk, and patient mortality. Monitoring NAC effects is necessary to capture resistant patients and stop or change treatment. The existing methods for evaluating NAC results have some limitations. The aim of this study was to assess the tumor response at an early stage, after the first doses of the NAC, based on the variability of the backscattered ultrasound energy, and backscatter statistics. The backscatter statistics has not previously been used to monitor NAC effects. Methods: The B-mode ultrasound images and raw radio frequency data from breast tumors were obtained using an ultrasound scanner before chemotherapy and 1 week after each NAC cycle. The study included twenty-four malignant breast cancers diagnosed in sixteen patients and qualified for neoadjuvant treatment before surgery. The shape parameter of the homodyned K distribution and integrated backscatter, along with the tumor size in the longest dimension, were determined based on ultrasound data and used as markers for NAC response. Cancer tumors were assigned to responding and non-responding groups, according to histopathological evaluation, which was a reference in assessing the utility of markers. Statistical analysis was performed to rate the ability of markers to predict the final NAC response based on data obtained after subsequent therapeutic doses. Results: Statistically significant differences (p<0.05) between groups were obtained after 2, 3, 4, and 5 doses of NAC for quantitative ultrasound markers and after 5 doses for the assessment based on maximum tumor dimension. Statistical analysis showed that, after the second and third NAC courses the classification based on integrated backscatter marker was characterized by an AUC of 0.69 and 0.82, respectively. The introduction of the second quantitative marker describing the statistical properties of scattering increased the corresponding AUC values to 0.82 and 0.91. Conclusions: Quantitative ultrasound information can characterize the tumor's pathological response better and at an earlier stage of therapy than the assessment of the reduction of its dimensions. The introduction of statistical parameters of ultrasonic backscatter to monitor the effects of chemotherapy can increase the effectiveness of monitoring and contribute to a better personalization of NAC therapy.

Purpose: To evaluate the ultrasound (US) response in patients with breast cancer (BC) during neoadjuvant chemotherapy (NAC). Methods: Prospective US analysis was performed on 19 malignant tumors prior to NAC treatment and 7 days after each first four courses of NAC in 13 patients (median age=57years). Echogenicity, size, vascularity, and sonoelastography were measured and compared with posttreatment scores of residual cancers burden. Results: Changes in the echogenicity of tumors after 3 courses of NAC had the most statistically strong correlation with the percentage of residual malignant cells used in histopathology to assess the response to treatment (odds ratio=60, p < 0.05). Changes in lesion size and elasticity were also significant (p < 0.05). Conclusions: There is a statistically significant relationship between breast tumors' echogenicity in US, neoplasm size, and stiffness and the response to NAC. In particular, our results show that the change in tumor echogenicity could predict a pathological response with satisfactory accuracy and may be considered in NAC monitoring.

breast ultrasonography, neoadjuvant chemotherapy, clinical response, breast cancer, sonoelastography

Objective: Neoadjuvant chemotherapy was initially used in locally advanced breast cancer, and currently it is recommended for patients with Stage 3 and with early-stage disease with human epidermal growth factor receptors positive or triple-negative breast cancer. Ultrasound imaging in combination with a quantitative ultrasound method is a novel diagnostic approach. Aim of study: The aim of this study was to analyze the variability of the integrated backscatter coefficient, and to evaluate their use to predict the effectiveness of treatment and compare to ultrasound examination results. Material and method: Ten patients (mean age 52.9) with 13 breast tumors (mean dimension 41 mm) were selected for neoadjuvant chemotherapy. Ultrasound was performed before the treatment and one week after each course of neoadjuvant chemotherapy. The dimensions were assessed adopting the RECIST criteria. Tissue responses were classified as pathological response into the following categories: not responded to the treatment (G1, cell reduction by ≤9%) and responded to the treatment partially: G2, G3, G4, cell reduction by 10–29% (G2), 30–90% (G3), >90% (G4), respectively, and completely. Results: In B-mode examination partial response was observed in 9/13 cases (completely, G1, G3, G4), and stable disease was demonstrated in 3/13 cases (completely, G1, G4). Complete response was found in 1/13 cases. As for backscatter coefficient, 10/13 tumors (completely, and G2, G3, and G4) were characterized by an increased mean value of 153%. Three tumors 3/13 (G1) displayed a decreased mean value of 31%. Conclusion: The variability of backscatter coefficient, could be associated with alterations in the structure of the tumor tissue during neoadjuvant chemotherapy. There were unequivocal differences between responded and non-responded patients. The backscatter coefficient analysis correlated better with the results of histopathological verification than with the B-mode RECIST criteria.

integrated backscatter coefficient (IBSCs), neoadjuvant chemotherapy (NAC), breast cancer, ultrasound

Commercially available cardiac scanners use 64–128 elements phased-array (PA) probes and classical delay-and-sum beamforming to reconstruct a sector B-mode image. For portable and hand-held scanners, which are the fastest growing market, channel count reduction can greatly decrease the total power and cost of devices. The introduction of ultra-fast imaging methods based on plane waves and diverging waves provides new insight into heart's moving structures and enables the implementation of new myocardial assessment and advanced flow estimation methods, thanks to much higher frame rates. The goal of this study was to show the feasibility of reducing the channel count in the diverging wave synthetic aperture image reconstruction method for phased-arrays. The application of ultra-fast 32-channel subaperture imaging combined with spatial compounding allowed the frame rate of approximately 400 fps for 120 mm visualization to be achieved with image quality obtained on par with the classical 64-channel beamformer. Specifically, it was shown that the proposed method resulted in image quality metrics (lateral resolution, contrast and contrast-to-noise ratio), for a visualization depth not exceeding 50 mm, that were comparable with the classical PA beamforming. For larger visualization depths (80–100 mm) a slight degradation of the above parameters was observed. In conclusion, diverging wave phased-array imaging with reduced number of channels is a promising technology for low-cost, energy efficient hand-held cardiac scanners.

phased-array, ultrasound imaging, diverging wave, synthetic transmit aperture

Streaming velocity depends on intensity and absorption of ultrasound in the media. In some cases, such as ultrasound scattered on blood cells at high frequencies, or the presence of ultrasound contrast agents, scattering affects the streaming speed. The velocities of acoustic streaming in a blood-mimicking starch suspension in water and Bracco BR14 contrast agent were measured. The source of the streaming was a plane 20MHz ultrasonic transducer. Velocity was estimated from the averaged Doppler spectrum. The single particle driving force was calculated as the integral of the momentum density tensor components. For different starch concentrations, the streaming velocity increased from 8.9 to 12.5mm/s. This corresponds to a constant 14% velocity increase for a 1 g/l increase in starch concentration. For BR14, the streaming velocity remained constant at 7.2mm/s and was independent of the microbubbles concentration. The velocity was less than in reference, within 0.5mm/s measurement error. Theoretical calculations showed a 16% increase in streaming velocity for 1 g/l starch concentration rise, very similar to the experimental results. The theory has also shown the ability to reduce the streaming velocity by low-density scatterers, as was experimentally proved using the BR14 contrast agent.

ultrasound, radiation force, starch, contrast agent, blood, thrombolysis

Attenuation of ultrasound in tissue can be estimated from the propagating pulse center frequency downshift. This method assumes that the envelope of the emitted pulse can be approximated by a Gaussian function and that the attenuation linearly depends on frequency. The resulting downshift of the mean frequency depends not only on attenuation but also on pulse bandwidth and propagation distance. This kind of approach is valid for narrowband pulses and shallow penetration depth. However, for short pulses and deep penetration, the frequency downshift is rather large and the received spectra are modified by the limited bandwidth of the receiving system. In this paper, the modified formula modeling the mean frequency of backscattered echoes is presented. The equation takes into account the limitation of the bandwidth due to bandpass filtration of the received echoes. This approach was applied to simulate the variation of the mean frequency of the pulse propagating for both weakly and strongly attenuating media and for narrowband and wideband pulses. The behavior of both the standard and modified estimates of attenuation has been validated using RF data from a tissue-mimicking phantom. The ultrasound attenuation of the phantom, determined with a corrected equation, was close to its true value, while the result obtained using the original formula was lower by as much as 50% at a depth of 8 cm.

Tissue attenuation, frequency downshift, bandwidth limitation

The soft tissue attenuation is an interesting parameter from medical point of view, because the value of attenuation coefficient is often related to the state of the tissue. Thus, the imaging of the attenuation coefficient distribution within the tissue could be a useful tool for ultrasonic medical diagnosis. The method of attenuation estimation based on tracking of the mean frequency changes in a backscattered signal is presented in this paper. The attenuation estimates are characterized by high variance due to stochastic character of the backscattered ultrasonic signal and some special methods must be added to data processing to improve the resulting images. The following paper presents the application of Spatial Compounding (SC), Frequency Compounding (FC) and the combination of both. The resulting parametric images are compared by means of root-mean-square errors. The results show that combined SC and FC techniques significantly improve the quality and accuracy of parametric images of attenuation distribution.

tissue attenuation estimation, parametric imaging, synthetic aperture, spatial compounding, frequency compounding

The paper presents the modified multi-element synthetic transmit aperture (MSTA) method for ultrasound imaging. It is based on coherent summation of RF echo signals with apodization weights taking into account the finite size of the transmit subaperture and of the receive element. The work presents extension of the previous study where the modified synthetic transmit aperture (STA) method was considered and verified [1]. In the case of MSTA algorithm the apodization weights were calculated for each imaging point and all combinations of the transmit subaperture and receive element using their angular directivity functions (ADFs). The ADFs were obtained from the exact solution of the corresponding mixed boundary-value problem for periodic baffle system modeling the transducer array. Performance of the developed method was tested using Field II simulated synthetic aperture data of point reflectors for 4 MHz 128-element transducer array with 0.3 mm pitch and 0.02 mm kerf to estimate the visualization depth and lateral resolution. Also experimentally determined data of the tissue-mimicking phantom (Dansk Fantom Service, model 571) obtained using 128 elements, 4 MHz, linear transducer array (model L14-5/38) and Ultrasonix SonixTOUCH Research platform were used for qualitative assessment of imaging contrast improvement. Comparison of the results obtained by the modified and conventional MSTA algorithms indicated 15 dB improvement of the noise reduction in the vicinity of transducer’s surface (1 mm depth), and concurrent increase in the visualization depth (86% augment of the scattered amplitude at the depth of 90 mm). However, this increase was achieved at the expense of minor degradation of the lateral resolution of approximately 8% at the depth of 50 mm and 5% at the depth of 90 mm.

Synthetic aperture imaging, Ultrasound imaging, Directivity function, Beamforming

Ultrasonic imaging is a well-established technique in medicine. However, in most conventional applications of clinical ultrasonic scanners only the peak amplitude echogenicity is used to create the image. Moreover, signal envelope detection destroys potentially useful information about frequency dependence of acoustic properties of tissue comprised in RF backscattered echoes. We have explored the possibility of developing the method of imaging the distribution of the acoustic attenuation in tissue. We expect that the method will help in localization of the pathological states of tissue including tumors and diffuse liver diseases. The spatial resolution and precision of the method are crucial for medical diagnosis, hence the synthetic aperture technique was applied for ultrasonic data collection. The final goal of the presented project is to develop reliable diagnostic tool, which could be implemented in standard USG systems, as the new visualization mode.

soft tissue parametric imaging, attenuation imaging, synthetic aperture focusing technique

The attenuating properties of biological tissue are of great importance in ultrasonic medical imaging. Investigations performed in vitro and in vivo showed the correlation between pathological changes in the tissue and variation of the attenuation coefficient. In order to estimate the attenuation we have used the downshift of mean frequency (fm) of the interrogating ultrasonic pulse propagating in the medium. To determine the fm along the propagation path we have applied the fm estimator (I/Q algorithm adopted from the Doppler mean frequency estimation technique). The mean-frequency shift trend was calculated using Single Spectrum Analysis. Next, the trends were converted into attenuation coefficient distributions and finally the parametric images were computed. The RF data were collected in simulations and experiments applying the synthetic aperture (SA) transmit-receiving scheme. In measurements the ultrasonic scanner enabling a full control of the transmission and reception was used. The resolution and accuracy of the method was verified using tissue mimicking phantom with uniform echogenicity but varying attenuation coefficient.

tissue attenuation imaging, synthetic aperture, diagnosis enhancing

Ultradźwiękowe metody badań nieniszczących przechodzą obecnie metamorfozę od systemów z głowicami jednoelementowymi do systemów wielokanałowych z głowicami fazowymi (PA Phased Array). Prowadzony obecnie w Zakładzie Ultradźwięków IPPT PAN projekt ma na celu opracowanie uniwersalnej wielokanałowej platformy ultradźwiękowej oraz metod rekonstrukcji obrazów mogących znaleźć zastosowanie zarówno w medycynie, jak i w badaniach nieniszczących. Przeprowadzono wstępne badania mające na celu porównanie różnych metod rekonstrukcji obrazów wad w trybie B-mode. W tym celu dokonano akwizycji ech ultradźwiękowych od wad w szynie kolejowej przy pomocy ultrasonografu badawczego wyposażonego w 128-elementową głowicą fazową o częstotliwości 4 MHz. Uzyskane sygnały ech wysokiej częstotliwości poddano następnie obróbce cyfrowej w celu uzyskania obrazu B-mode. Zastosowano i porównano różne metody rekonstrukcji obrazu: klasyczny beamforming oraz metodę syntetycznej apertury. Wstępne wyniki wskazują na wysoką jakość rekonstrukcji metodą syntetycznej apertury, która zapewnia równomierną rozdzielczość poprzeczną w całej głębokości obrazowania. Zastosowanie alternatywnych schematów nadawczo-odbiorczych w metodzie syntetycznej apertury umożliwia dodatkowo optymalizację metody pod względem prędkości badania lub jakości obrazowania. Wyniki te potwierdzają przydatność i konkurencyjność metody syntetycznej apertury do stosowanej obecnie metody beamformingu.

ultradźwiękowe badania nieniszczące, głowice Phased-Array, metody syntetycznej apertury

The mean frequency correlation estimator and SSA technique were implemented for processing of the RF ultrasonic echoes. The estimated attenuation values were equal to 0.7 and 0.9 dB/(MHz∙cm) and agreed well with the real values. We have found the RF data obtained using synthetic aperture technique (SA) to be much more reliable in terms of attenuation extraction then echoes recorded using the standard delay and sum (DAS) beamforming. The imaging of attenuation in tissue seems to be a promising technique in medical diagnostics, although the precision of a single scan is often unsatisfactory.

tissue attenuation imaging, sythetic aperture focusing technique

Ultrasonic methods of human body internal structures imaging are being continuously enhanced. New algorithms are created to improve certain output parameters. A synthetic aperture method (SA) is an example which allows to display images at higher frame-rate than in case of conventional beam-forming method. Higher computational complexity is a limitation of SA method and it can prevent from obtaining a desired reconstruction time. This problem can be solved by neglecting a part of data. Obviously it implies a decrease of imaging quality, however a proper data reduction technique would minimize the image degradation. A proposed way of data reduction can be used with synthetic transmit aperture method (STA) and it bases on an assumption that a signal obtained from any pair of transducers is the same, no matter which transducer transmits and which receives. According to this postulate, nearly a half of the data can be ignored without image quality decrease. The presented results of simulations and measurements with use of wire and tissue phantom prove that the proposed data reduction technique reduces the amount of data to be processed by half, while maintaining resolution and allowing only a small decrease of SNR and contrast of resulting images.

ultrasonic imaging, synthetic transmit aperture, data reduction, effective aperture, reciprocity

The pathological states of biological tissue are often resulted in attenuation changes. Thus, information about attenuating properties of tissue is valuable for the physician and could be useful in ultrasonic diagnosis. We are currently develop ing a technique for parametric imaging of attenuation and we intend to apply it for in vivo characterization of tissue. The attenuation estimation method based on the echoes mean frequency changes due to tissue attenuation dispersion, is presented. The Doppler IQ technique was adopted to estimate the mean frequency directly from the raw RF data. The Singular Spectrum Analysis technique was used for the extraction of mean frequency trends. These trends were converted into atten uation distribution and finally the parametric images were computed. In order to reduce variation of attenuation estimates the spatial compounding method was applied. Operation and accuracy of attenuation extracting procedure was verified by calculating the attenuation coefficient distribution using the data from the tissue phantom (DFS, Denmark) with uniform echogenicity while attenuation coefficient underwent variation.

ultrasound attenuation estimation, spatial compounding, parametric imaging

The Synthetic Aperture (SA) methods are widespread and successfully used in radar technology, as well as in the sonar systems. The advantages of high framerate and its relatively good resolution in the whole area of scanning, make this technique an object of interest in medical imaging methods such as ultrasonography (US). This paper describes the possible usage of the SA method in ultrasound imaging. The introduction to the principles of the SA technique in ultrasonography is presented. The measurements of different SA schemes were conducted using the set-up consisting of the research ultrasonograph module, the PC and the special wire phantom. The results for different schemes of image reconstruction are presented. Particularly the Synthetic Transmit Aperture (STA) technique was concerned. Results of the STA method are discussed in this paper.

synthetic aperture focusing technique, ultrasonic imaging

Ultrasonography (USG) is a widespread and powerful tool used successfully in modern diagnostics. The standard USG scanner reflects impedance variations within the tissue that is penetrated by the ultrasound pulse. Although such image provides a lot of information to the physician, there are another parameters which could be imaged. The attenuation coefficient is one of them. Imaging of attenuation seems to be a promising tool for ultrasonic medical diagnostics. The attenuation estimation method based on the echoes mean frequency changes due to tissue attenuation dispersion is presented. The Doppler IQ technique is adopted to estimate the mean frequency changes directly from the raw RF data. The Singular Spectrum Analysis (SSA) technique is used for the mean frequency trend extraction. The changes of the mean frequency trend are related directly to the local attenuation coefficient. Preliminary results of the tissue phantom attenuation coefficient estimation and imaging using the commercial scanner are presented.

tissue attenuation imaging, ultrasound attenuation estimation

The attenuating properties of biological tissue are of great importance in ultrasonic examination even though its anatomical variability limits diagnostics effectiveness. We are currently developing a technique for parametric imaging of attenuation and we intend to apply it for in vivo characterization of tissue. The diagnostic usefulness of the proposed technique crucially depends on the precision of the attenuation estimate and the resolution of the parametric image. These two parameters are highly correlated, since the resolution is reduced whenever averaging is used to minimize the errors introduced by the random character of the backscatter. Here we report on the results of numerical processing of both, simulated and recorded from a tissue-mimicking phantom echoes. We have analyzed the parameters of the estimation technique and examined their influence on the precision of the attenuation estimate and on the parametric image resolution. The optimal selection of attenuation image parameters depending on its intended diagnostic use, was also considered.

ultrasound attenuation, spatial resolution, parametric imaging

The gas filled polymer spheres are used either as an ultrasonic contrast agents or controlled drug delivery microcapsules. The power spectrum of the ultrasonic backscattered signal was calculated from the resonance scattering theory for the gas bubbles surrounded by elastic shield. The size distribution of the measured microspheres was included in the calculations. In experiment, the backscattered power spectrum of measured sample was recorded by Siemens Antares ultrasonic scanner. Radio frequency (RF) data was recorded for 2.5 – 6.7 MHz transmitted ultrasonic frequencies. The backscattered spectra were calculated by Matlab software and subtracted from the transmitter spectrum, recorded as an echo from the perfect reflector. The particle size in measured sample was 12 μm mean ± 8 μm sd. The resonance frequency, measured under the microscope, was 0.60 MHz for 45 μm diameter microsphere which corresponds to 2.25 MHz for 12 μm sphere. The sample volume was 10cm³ and the mean quantity of scatterers was 6·103/cm³. In conclusion, measured spectra matched those calculated from theory. The use of ultrasonic scanner with RF data output and the high sensitivity, wide bandwidth ultrasonic transducer allows to measure backscattered signal from the very small quantity of resonance scatterers with satisfactory results at 40 dB signal to noise ratio.

ultrasound, ultrasonic contrast agents, microcapsules, resonance frequency

Coded ultrasonography is intensively studied in many laboratories due to its remarkable properties, particularly increased penetration depth and signal-to-noise ratio (SNR). However, no data on the spatial behavior of the pressure field generated by coded bursts transmissions in the tissue were yet reported. This paper reports the results of investigations of the field structure in water, in degassed beef liver and in pork tissue using four different excitations signals, two and 16 periods sine bursts and sinusoidal sequences with phase modulation using 13-bits Barker code and 16-bits Golay complementary codes. The results of measured pressure field distributions before and after compression were compared with those recorded using short pulse excitation.

Coded excitation, Ultrasound field distribution, Matching filtering

A standard USG image is in fact a visualization of a distribution of the reflexion coefficients. There is an increasing interest in imaging of the different parameters, which might characterize another physical properties of a tissue. The attenuation coefficient is one of such parameters and theoretically it can be estimated using frequency shift of the RF signal. The frequency shift results from dispersive character of the attenuation in tissue and is a function of attenuation along the propagate path. In this work authors use echo’s mean frequency as an imaging modality. The results of measurement of tissue phantom using 10 MHz linear array are presented. The preliminary results are encouraging being the first attempt towards mapping of the attenuation in tissue.

parametric visualization, mean frequency, attenuation estimation

This work reports the results of measurements of spatial distributions of ultrasound fields obtained from five energizing schemes. Three different codes, namely, chirp signal and two sinusoidal sequences were investigated. The sequences were phase modulated with 13 bits Barker code and 16 bits Golay complementary codes. Moreover, two reference signals generated as two and sixteen cycle sine tone bursts were examined. Planar, 50% (fractional) bandwidth, 15 mm diameter source transducer operating at 2 MHz center frequency was used in all measurements. The experimental data were collected using computerized scanning system and recorded using wideband, PVDF membrane hydrophone (Sonora 804). The measured echoes were compressed, so the complete pressure field in the investigated location before and after compression could be compared. In addition to a priori anticipated increase in the signal to noise ratio (SNR) for the decoded pressure fields, the results indicated differences in the pressure amplitude levels, directivity patterns, and the axial distance at which the maximum pressure amplitude was recorded. It was found that the directivity patterns of non-compressed fields exhibited shapes similar to the patterns characteristic for sinusoidal excitation having relatively long time duration. In contrast, the patterns corresponding to compressed fields resembled those produced by brief, wideband pulses. This was particularly visible in the case of binary sequences. The location of the maximum pressure amplitude measured in the 2 MHz field shifted towards the source by 15 mm and 25 mm for Barker code and Golay code, respectively. The results of this work may be applicable in the development of new coded excitation schemes. They could also be helpful in optimizing the design of imaging transducers employed in ultrasound systems designed for coded excitation. Finally, they could shed additional light on the relationship between the spatial field distribution and achievable image quality and in this way facilitate optimization of the images obtained using coded systems.

coded excitation, sound fields

Coded ultrasonography is intensively studied in many laboratories due to its remarkable properties: increased depth penetration, signal-to-noise ratio (SNR) gain and improved axial resolution. However, no data concerning the spatial behavior of the pressure field generated by coded bursts transmissions were reported so far. Five different excitation schemes were investigated. Flat, circular transducer with 15 mm diameter, 2 MHz center frequency and 50% bandwidth was used. The experimental data was recorded using the PVDF membrane hydrophone and collected with computerized scanning system developed in our laboratory. The results of measured pressure fields before and after compression were then compared to those recorded using standard ultrasonographic short-pulse excitation. The increase in the SNR of the decoded pressure fields is observed. The modification of the spatial pressure field distribution, especially in the intensity and shape of the sidelobes is apparent. Coded sequences are relatively long and, intuitively, the beam shape could be expected to be very similar to the sound field of long-period sine burst. This is true for non-compressed distributions of examined signals. However, as will be shown, the compressed sound fields, especially for the measured binary sequences, are similar rather to field distributions of short, wideband bursts.

coded excitation, ultrasonic field distribution, pulse compression, matched filtration, medical imaging

Background, Motivation and Objective
Flexible ultrasound (US) arrays are a promising technology that may further democratize US technology — e.g. in wearable US. Flexible transducers also pose challenges in image reconstruction, as they require adaptable beamforming delays due to a changing geometry of the probe. Various approaches have been proposed for flexible array shape estimation and beamforming, e.g. external sensors, deep learning and optimization. In this work, we propose a deep reinforcement learning (DRL) approach, where a software agent is responsible for tracking the array shape to properly reconstruct US B-mode image.

Statement of Contribution/Methods
Here we considered a reinforcement learning environment as a setup consisting of a US system with the flexible array and point targets phantom. The environment was simulated using j-Wave software. The environment’s state consisted of the current shape of the array, modeled as a sinusoid s = a sin(bx + c), and the current model of the array assumed by the beamformer: s’ = a’ sin(b’x + c’) (single-element STA scheme). A single episode consisted of
7 steps; the parameters a, b, c could vary from step to step (within bounds of physical constraints). The agent observed the current B-mode, could modify the current values of a’, b’ and c’ (action) and received a reward equal to the linear combination of the coherence factor and structural similarity index measure (SSIM) between the current and reference image. We trained our agent using TD3 approach and tested it for various settings of a, b and c.

Results/Discussion
Our agent achieved an average SSIM of 0.73 per episode step. Figure 1 shows the sequence of states and images within an example episode; the agent was able to correctly react to the change of the array shape. The DRL approach has the following advantages compared to other methods: the agent can be trained to operate in an environment with a changing state; and the agent can be trained to maximize expected return (dependent on beamforming quality metric), which does not have to be differentiable.

Fig. 1. (Top) B-mode images, estimated array shape (red) and actual shape (black) within an example episode. (Bottom) Coherence factor and SSIM achieved by the agent during the episode

Accurate estimation of the speed-of-sound (SoS) is important for ultrasound (US) image reconstruction techniques and tissue characterization. Various approaches have been proposed to calculate SoS, ranging from tomography-inspired algorithms like CUTE to convolutional networks, and more recently, physics-informed optimization frameworks based on differentiable beamforming. In this work, we utilize implicit neural representations (INRs) for SoS estimation in US. INRs are a type of neural network architecture that encodes continuous functions, such as images or physical quantities, through the weights of a network. Implicit networks may overcome the current limitations of SoS estimation techniques, which mainly arise from the use of non-adaptable and oversimplified physical models of tissue. Moreover, convolutional networks for SoS estimation, usually trained using simulated data, often fail when applied to real tissues due to out-of-distribution and data-shift issues. In contrast, implicit networks do not require extensive training datasets since each implicit network is optimized for an individual data case. This adaptability makes them suitable for processing US data collected from varied tissues and across different imaging protocols. We evaluated the proposed SoS estimation method based on INRs using data collected from a tissue-mimicking phantom containing four cylindrical inclusions, with SoS values ranging from 1480 m/s to 1600 m/s. The inclusions were immersed in a material with an SoS value of 1540 m/s. In experiments, the proposed method achieved strong performance, clearly demonstrating the usefulness of implicit networks for quantitative US applications.

beamforming, deep learning, implicit neural representations, speed-of-sound, quantitative ultrasound

We investigate the usefulness of formula-driven supervised learning (FDSL) for breast ultrasound (US) image analysis. Medical data are usually too scarce to develop a better performing deep learning model from scratch. Transfer learning with networks pre-trained on ImageNet is commonly applied to address this problem. FDSL techniques have been recently investigated as an alternative solution to ImageNet based approaches. In the FDSL setting, networks for transfer learning applications are developed using large amounts of synthetic images generated with mathematical formulas, possibly taking into account the characteristics of the target data. In this work, we use Field II to develop a large synthetic dataset of 100 000 US images presenting different contour objects, as shape features play an important role in breast mass characterization in US. Synthetic data are utilized to pre-train the ResNet50 classification model and various variants of the U-Net segmentation network. Next, the pre-trained models are fine-tuned on breast mass US images. Our results demonstrate that the proposed FDSL approach can provide good performance with respect to breast mass classification and segmentation.

breast cancer, deep learning, synthetic data, ultrasound

Standard classification methods based on hand-crafted morphological and texture features have achieved good performance in breast mass differentiation in ultrasound (US).
In comparison to deep neural networks, commonly perceived as ‘black-box’ models, classical techniques are based on features that have well-understood medical and physical interpretation. However, classifiers based on morphological features commonly
underperform in the presence of the shadowing artifact and ill-defined mass borders, while texture based classifiers may fail when the US image is too noisy. Therefore, in practice it would be beneficial to select the classification method based on the appearance of the particular US image. In this work, we develop a deep meta-network that can automatically process input breast mass US images and recommend whether to apply the shape or
texture based classifier for the breast mass differentiation. Our preliminary results demonstrate that meta-learning techniques can be used to improve the performance of the standard classifiers based on handcrafted features. With the proposed meta-learning based approach, we achieved the area under the receiver operating characteristic curve of 0.95 and accuracy of 0.91.

breast mass classification, deep learning, meta-learning, morphological features, texture features

Ultrasound (US) imaging is widely used for the tissue characterization. However, US images commonly suffer from speckle noise, which degrades perceived image quality. Various deep learning approaches have been proposed for US image denoising, but most of them lack the interpretability of how the network is processing the US image (black box problem). In this work, we utilize a deep reinforcement learning (RL) approach, the pixelRL, to US image denoising. The technique utilizes a set of easily interpretable and commonly used filtering operations applied in a pixel-wise manner. In RL, software agents act in an unknown environment and receive appropriate numerical rewards. In our case, each pixel of the input US image has an agent and state of the environment is the current US image. Agents iteratively denoise the US image by executing the following pixel-wise pre-defined actions: Gaussian, bilateral, median and box filtering, pixel value increment/decrement and no action. The proposed approach can be used to generate action maps depicting operations applied to process different parts of the US image. Agents were pre-trained on natural gray-scale images and evaluated on the breast mass US images. To enable the evaluation, we artificially corrupted the US images with noise. Compared with the reference (noise free US images), filtration of the images with the proposed method increased the average peak signal-to-noise ratio (PSNR) score from 14 dB to 26 dB and increased the structure similarity index score from 0.22 to 0.54. Our work confirms that it is feasible to use pixel-wise RL techniques for the US image denoising.

deep reinforcement learning, ultrasound imaging, image denoising, filtration, breast cancer

Precise quantitative analysis of the sonothrombolysis process is required to minimise the amount of thrombolytic drug dangerous for the patient, because it can cause internal hemorrhage. Verification of the effects of other drugs or other procedures for the elimination of thrombi, for example ultrasound contrast microbubbles, also requires quantitative research. For microscopic examination of the thrombolysis process, the Rexolite parallel plate flow chamber has been used. The internal dimensions of the chamber were 11x1x20 mm. In order to eliminate the standing wave, the incident wave was perpendicular to the reflected one. The narrowband chirp driven transducer suppressed the surface waves in Rexolite. The clot dissolution was processed at 40 kHz – 6 MHz ultrasound frequencies and 2 W/cm2 spatial averaged, temporal averaged intensities. The thrombus was obtained from a 5 μl drop of blood placed directly inside a flow chamber. The flow chamber was filled with the cell culture medium Dulbecco's modified Eagle's medium. The flow in the chamber was forced by a peristaltic pump at a speed of 3.8 ml/min. The Actilyse tissue plasminogen activator at a concentration of 10 μg/ml was added. The similarity of the thrombolysis process obtained from 5 μl of blood with a similar volume fragment cut from a larger thrombus was experimentally verified. Thrombus volume was estimated from microscopic photographs by calculating its surface area and its optical transparency. At 2 W/cm2 ultrasound intensity, took the thrombus 4, 8, 9 and 12 minutes to completely dissolve for the centre ultrasound frequencies of 40.9, 149, 209 kHz and 1.02 MHz, respectively. For higher frequencies, the thrombus only reduced its volume by 82%, 69% and 27% for the frequencies 2.10, 3.34 and 6.63 MHz, respectively. Sonication for 20 - 60 minutes did not cause further dissolution of thrombi.

ultrasound, blood, thrombus, thrombolysis, parallel plate flow chamber

Neoadjuvant chemotherapy (NAC) is used in breast cancer patients to reduce tumor size, decrease the risk of local recurrence, and diminish the likelihood of metastases, all of which reduce patient mortality. Assessment of the response to NAC at an early stage of treatment allows therapy to be personalized. The quantitative ultrasound method is a novel diagnostic approach with great potential to improve outcomes for breast cancer patients. The aim of this study was to analyze the variability of the integrated backscatter coefficient (IBSC) in the context of the assessment of changes in tumor tissue structure resulting from chemotherapy, and to evaluate use of IBSC to predict the effectiveness of treatment. Ultrasound data (B-mode images and raw ultrasonic radio frequency signals RF) were collected from cancerous tumors from patients intended to NAC before starting treatment and a week after each chemotherapy dose. Data were processed to generate IBSC maps and to determine IBSC mean values. The assessment of tumors using IBSC, in comparison with histopathological verification, showed that IBSC changes can be associated with changes which tumor structure changes during NAC. IBSC analysis correlated better with the results of histopathological verification than B-mode imaging.

Under the influence of pathological changes, the blood coagulates inside the blood vessel, creating a thrombus. The thrombus dissolution process is called thrombolysis. The aim of the study is to evaluate the thrombolysis process by the interaction of the thrombolitic drug and ultrasound. The clot dissolution process was analyzed in a specially designed, transparent for ultrasound parallel plate flow chamber. Inside, a freshly coagulated human blood sample was exposed to ultrasound. A liquid containing the tissue plasminogen activator drug in a concentration of 10 μg/ml passed around the sample. The liquid flow was forced by a peristaltic pump. The source of ultrasound was a 1 MHz flat ultrasonic transducer with a 25 mm diameter. The transducer radiated a 1000 periods burst, repeated every 2500 periods and space averaged time averaged intensities of 0.2-1.6 W/cm2. The efficacy of thrombus dissolution was observed by means of a designed parallel plate flow chamber and the time of thrombus complete dissolution was measured. The best result for the 1 MHz frequency and space averaged time averaged intensity of 1.6 W/cm2 was recorded, where the thrombus was dissolved within 5.5 minutes.

ultrasound, blood, thrombus, thrombolysis, parallel plate flow chamber

Parallel plate flow chambers are widely used for cell research in conditions of constant or pulsatile liquid flow. They are also used to analyze the formation of thrombi. The authors designed a flow chamber that is transparent to ultrasound, thus enabling the microscopic observation of the thrombus dissolution process by interaction of drug, microbubbles and ultrasound in real time, in-vitro. Sonothrombolysis was performed at 1 MHz and 3.3 MHz ultrasound frequencies, at space-averaged and time-averaged intensities ISATA of 0.2 – 1.6 W/cm2. 1 mm thick slices of the human blood clots were exposed to ultrasound in the presence of the Actilyse tissue plasminogen activator at a concentration of 10 μg/ml flowing around the sample. Next, the effect of SonoVue microbubbles at a concentration of 5x105/ml on the dissolution of the thrombus was investigated. Thrombus size changes were observed under a 4x magnification microscope and were recorded as a function of time. The best result was achieved for the Actilyse tissue plasminogen activator at the 1 MHz and 1.6 W/cm2 ISATA, the thrombus was sonolysed within 5.5 minutes. The noticeable effect of the SonoVue microbubbles on the thrombolysis process appeared at 3.3 MHz, the thrombus was sonolysed in 12 min at ISATA = 1.6 W/cm2.

ultrasound, blood, thrombus, thrombolysis, parallel plate flow chamber

Breast cancer is one of the leading causes of cancer-related death in female patients. The quantitative ultrasound techniques being developed recently provide useful information facilitating the classification of tumors as malignant or benign. Quantitative parameters are typically determined on the basis of signals scattered within the tumor. The present paper demonstrates the utility of quantitative data estimated based on signal backscatter in the tissue surrounding the tumor. Two quantitative parameters, weighted entropy and Nakagami shape parameter were calculated from the backscatter signal envelope. The ROC curves and the AUC parameter values were used to assess their ability to classify neoplastic lesions. Results indicate that data from tissue surrounding the tumor may characterize it better than data from within the tumor. AUC values were on average 18% higher for parameters calculated from data collected from the tissue surrounding the lesion than from the data from the lesion itself.

uantitative ultrasound, Tissue characterisation, Tumor classification

Streaming velocity mainly depends on the intensity and absorption of ultrasound in the media. For high frequencies exceeding 20 MHz the speed of streaming in blood is also affected by scattering effects on the blood cells and contrast agent microbubbles. According to theoretical calculations, 12.2 % increase in streaming velocity for 1 g/l starch concentration rise should be expected. The theory has also shown the reduction of the streaming velocity by low-density scatterers, estimated decrease was -9.7 % for BR14 contrast agent. The experimental measurements of streaming velocities were done in blood mimicking fluid and Bracco BR14 microbubbles dissolved in water. The streaming was generated by a plane 20 MHz ultrasonic transducer driven by a pulsed Doppler flowmeter. For starch concentration changing from 0.01g/l (reference fluid) up to 1g/l the streaming velocity increased by 13%, very close to the theoretical prediction. For BR14, the measured velocity was 9% less than in reference fluid and remained independent on the microbubbles concentration.

streaming, radiation force, starch, contrast agent, blood

This paper is a part of the IUS 2016 Planewave Imaging Challenge submission. Spatial compounding of several low-resolution images taken from different angles is a well-established practice in ultrasound imaging. The usual procedure relies on averaging several images using arithmetic mean. An alternative approach is presented, which relies on multiplication of the low-resolution images.

ultrasound, plane wave imaging

The parametric imaging can enhance ultrasonic examinations that are widely used in medical diagnostics. Attenuation of the wave propagating through the soft tissue reflects the state of the tissue, what is clearly demonstrated in literature. The visualization of the spatial distribution of attenuation may support the diagnosis by accurate discrimination of the lesions from normal tissue at the early stage of the disease. This research is focused on the developing of the method of attenuation estimation from ultrasonic backscatter. It would allow to produce the parametric images from the same data as the standard B-mode images. The attenuation estimation method bases on the spectral mean frequency (fm) downshift of the propagating pulse, that results from the frequency dependence of attenuation. The fm was determined (using fm correlation estimator and trend extraction with Single Spectrum Analysis algorithm) from the ultrasonic echoes scattered in the tissue mimicking phantom which contained a cylinder with the attenuation coefficient higher than in the background. The data acquisition were performed using ultrasonic scanner. The earlier research indicated the effectiveness of attenuation estimation method using the synthetic aperture technique to collect the data. The use of the synthetic transmit aperture scheme to acquire the data results in better attenuation imaging comparing to standard beamforming, however it lower the penetration depth. In this research the compounded plane wave transmit-receiving scheme was used, to improve the penetration range. Compensation for the diffraction effects was included in the data processing. The results indicate suitability of this approach for attenuation imaging. We can measure attenuation in the tissue mimicking materials with the spatial resolution of approximately 10mm and accuracy of 0.2dB/(MHz·cm). In the presentation, the attenuation images of tissue mimicking phantoms and the images of human liver, obtained in vivo, will be presented.

attenuation imaging, plane wave imaging

A modified multi-element synthetic transmit aperture (MSTA) method for ultrasound imaging with RF echoes correction taking into account the influence of the element directivity is presented. The property is significant as the element width becomes commensurable with the wavelength of the emitted signal. The angular dependence of the radiation efficiency of the transmit/receive aperture is obtained from exact solution of the corresponding mixed boundary-value problem for periodic baffle system, modeling the transducer array. It is evaluated at the nominal frequency of the excitation signal and is implemented in the developed MSTA algorithm as apodization weights calculated for each imaging point and all combinations of the transmit/receive apertures. The performance of developed method is tested using FIELDII simulated synthetic aperture data of the point reflectors to estimate the visualization depth and lateral resolution. Besides, a FIELDII simulated and measurement data of cyst phantom are used for qualitative assessment of the imaging contrast. Comparison of the results obtained by the modified and conventional MSTA algorithms is given which reveals considerable improvement of the image quality in the area neighboring to the transducer’s aperture, and increase of the visualization depth at the cost of slight degradation of lateral resolution near the transducer face.

synthetic aperture, ultrasound imaging, beamforming

The attenuation of tissue can be estimated utilizing the downshift of the center frequency of a propagating pulse. In general it is assumed that the shape of the emitted pulse can be approximated by a Gaussian function and attenuation is assumed to change linearly with frequency. At this conditions the downshift of the mean frequency of pulse spectrum depends linearly on attenuation coefficient, pulse bandwidth and propagation distance. This is a good approximation for relatively narrowband pulses and small penetration depth. But for short pulses and deep penetration the frequency downshift is large and the ultrasonic pulse is no more Gaussian, thus the previous assumption is no longer correct. The closer is the mean frequency of the pulse to the lower frequency bound of the receiving system the bigger deformation of the pulse spectrum occurs and consequently the attenuation is determined with bigger error. The following paper presents how to correct the experimentally determined mean frequency and to obtain reliable results when investigating tissue attenuation with wideband pulses. We propose a new formula for the dependence between pulse mean frequency, tissue attenuation, pulse bandwidth and traveled distance. The formula was derived from the mean frequency of Gaussian pulse spectrum determined in the limited frequency band. The formula was applied to simulate variation of mean frequency MF of the pulse propagating in the medium with attenuation coefficient corresponding to the attenuation in the tissue mimicking phantom. The MF was also determined (using the correlation estimator of MF and next trend extraction using Single Spectrum Analysis) from the simulated ultrasonic echoes and echoes scattered in the tissue phantom. The corrected nonlinear formula describes well MF variation along the pulse propagation path. The departure from the linear dependence increases with large MF shift, thus it is well pronounced for highly attenuating tissue, the wideband pulses and deep penetration.

attenuation estimation, frequency downshift

The long term goal of this research is to develop the system enabling the imaging and quantitative measure of ultrasonic attenuation in tissue. It may support the diagnosis by accurate discrimination of the lesions from normal tissue at the early stage of the disease. The attenuation is estimated from the stochastic ultrasonic backscatter and time/spatial averaging is necessary to achieve reasonable accuracy. However the averaging worsens the spatial resolution. Two techniques of ultrasonic imaging, the Synthetic Aperture Focusing technique (SAFT) and Spatial Compounding (SC), were applied and compared with respect to the quality of attenuation estimation. The ultrasonic RF data were collected from a tissue mimicking phantom using ultrasonic scanner (Ultrasonix SonixTOUCH). Both acquired echoes-sets were processed in the same way in order to calculate the downshift in a mean frequency fm of the backscatter signal and resulting spatial distribution of attenuation coefficient. Compensation for the diffraction effects was included in the data processing. The RF data obtained with use of the SAFT proved to be more suitable for attenuation estimation.

tissue attenuation imaging, synthetic aperture, spatial compounding

The synthetic aperture focusing techniques (SAFT) are well known and widely deployed in radar techniques. Increasing processing power of modern computers allows effective implementation of various SAFT schemes in medical ultrasound systems with multi-element probes. The advance of the SAFT over the classical beamforming (BFR) is dynamic focusing in receive and transmit as well, which brings high resolution on every imaging point. We intend to develop the ultrasound imaging platform with some of the SAFT schemes implemented. The choice of the scheme must be preceded by examination of the imaging quality parameters. The results of the comparison of different SAFT schemes with conventional beamforming are presented in the paper. The results indicate that SAFT schemes can work better than BFR scheme. For example, for some point located near the center of the image the full width at half maximum (FWHM) was equal approximately 0.5, 0.4 and 0.3 mm, while the contrast-to-noise ratio (CNR) was equal 18, 19, and 19 dB for BFR, STA and PWI respectively.

Ultrasonic imaging, SAFT, Plane Wave Imaging

Standardowe obrazy ultrasonograficzne przestawiają rozkład zmian impedancji akustycznej wewnątrz tkanki. Możliwe jest jednak obrazowanie innych parametrów akustycznych. Takie parametryczne obrazy dostarczałyby dodatkowych informacji o stanie tkanki, przydatnych w diagnostyce. Zmiany wartości współczynnika tłumienia fali ultradźwiękowej często towarzyszą patologicznym zmianom struktury tkanki, np. nowotworom. Jedną z metod estymacji tłumienia jest metoda bazująca na przesunięciu częstotliwości średniej widma sygnału w czasie jego propagacji w tkance. Główną niedogodnością metody jest duża wariancja otrzymywanych estymat. W niniejszej pracy przedstawiono rezultaty zastosowania metod uśredniania przestrzennego i częstotliwościowego w celu polepszenia jakości otrzymywanych obrazów rozkładu tłumienia. Weryfikacje metod przeprowadzono w oparciu o dane symulacyjne i dane z fantomu tkankowego o stałej echogeniczności i zmiennym tłumieniu.

estymacja tłumienia, uśrednianie przestrzenne, uśrednianie częstotliwościowe

Increasing efforts are currently dedicated to incorporate the Synthetic Transmit Aperture (STA) method in ultrasound imaging systems. The STA technique can provide a pixel-like focusing (dynamic focusing at both transmit and receive) without impairment of the frame rate. This goal can only be achieved by a careful design of the transmission and reception schemes. In this paper, the preliminary results of resolution, Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) measurements for a specific SA scheme with different transmit apertures are presented. The measurements were made using a novel ultrasound advanced open platform (ULA-OP) developed at the University of Florence. The ULA-OP is fully programmable and enables access to the RF echo-data from each transducer element.

synthetic aperture, ultrasound open platform

The pathological states of biological tissues are often connected with attenuation changes. Thus, information about attenuating properties of tissue is valuable for the physician and could be useful in ultrasonic diagnosis. We are currently developing a technique for parametric imaging of attenuation and we intend to apply it for in vivo characterization of tissue. The attenuation estimation method based on the echoes mean frequency hanges due to tissue attenuation dispersion is presented. The Doppler IQ technique was adopted to estimate the mean frequency directly from the raw RF data. The Singular Spectrum Analysis technique was used for the mean frequency trends extraction. These trends were converted into attenuation distribution and finally the parametric images were computed. In order to reduce variation of attenuation estimates the spatial compounding method was applied. Operation and accuracy of attenuation extracting procedure was verified by calculating the attenuation coefficient distribution using the data from the tissue phantom with uniform echogenicity but varying attenuation coefficient (DFS, Denmark).

tissue attenuation estimation, spatial compounding

Metody ultradźwiękowego obrazowania struktur wewnętrznych człowieka są nieustannie ulepszane. Opracowywane są nowe algorytmy umożliwiające poprawę niektórych parametrów wynikowych. Przykładem jest metoda syntetycznej apertury (SA), która pozwala wyświetlać obrazy o wysokiej rozdzielczości z większą częstotliwością niż standardowa metoda formowania wiązki.
Ograniczeniem metody SA jest większa złożoność obliczeniowa, która przy znacznej ilości danych może uniemożliwić osiągnięcie pożądanej szybkości rekonstrukcji. Problem ten można ominąć rezygnując z części zarejestrowanych sygnałów. Wiąże się to oczywiście z pogorszeniem jakości obrazowania, jednak przy odpowiedniej technice przerzedzania danych degradacja obrazu jest niewielka. Proponowany sposób przerzedzania ma zastosowanie w metodzie syntetycznej apertury nadawczej (STA) i opiera się na założeniu, że sygnał uzyskany za pomocą dowolnej pary przetworników jest taki sam niezależnie od tego, który z przetworników nadawał, a który odbierał. W tej sytuacji można pominąć blisko połowę zapisanych danych i wciąż
możliwe będzie zrekonstruowanie obrazu w poprzedniej jakości. Na podstawie prezentowanych wyników symulacji oraz pomiarów na fantomach nitkowym i tkankowym wykazano, że stosując opisaną technikę przerzedzania można zmniejszyć o połowę ilość danych przy zachowaniu rozdzielczości i nieznacznym spadku parametru SNR oraz kontrastu rekonstruowanych obrazów.

metody syntetycznej apertury, przerzedzanie danych

The Synthetic Aperture (SA) methods are widespread and successfully used in radar technology, as well as in the sonar systems. The advantages of relatively good resolution in the whole area of scanning without decrease of framerate, make this technique an object of interest in medical imaging methods such as ultrasonography (US).This paper describes the possible usage of the SA method in ultrasound imaging. The measurements of different SA schemes were conducted using the set-up consisting of the research ultrasonograph module, the PC and the special wire phantom. The results for different schemes of image reconstruction are presented. Particularly the Synthetic Transmit Aperture (STA) technique was concerned. Results of the STA method are discussed in this paper.

ultrasonic imaging, synthetic aperture focusing technique

Determination of attenuating properties of the tissue from the echoes of waves emitted by the focused transducer requires to compensate the echo signal for the effects of focusing. We propose the diffraction/focusing effects correcting (FC) technique that compensates focusing-induced mean frequency
shift (MFS) of the propagating pulse. The method corrects mean frequency estimates derived from echo pulses propagating in attenuating tissue with locally varying attenuation coefficient. The FC algorithm applies the diffraction correcting coefficients obtained experimentally from the probing pulses focused in water for assessing the expected values of MFS of pulses focused in attenuating tissue. The calculations involves the pulse ‘history’ that due to overall attenuation along the traveled path downshifts the pulse spectrum resulting in variation of the focusinginduced MFS obtained in water.

attenuation estimation, difraction correction, parametric imaging

Background: Monitoring of response to neoadjuvant chemotherapy (NAC) in advanced breast cancer is crucial for assessing the effectiveness of the treatment and overall survival. The purpose of this study was to investigate the ability of classical ultrasound (US) examination and quantitative ultrasound (QU) parameters to predict the therapy response comparing to histology results after surgical treatment. Material and Methods: Two ultrasound backscatter parameters: the integrated backscatter coefficient, (IBSC) and shape parameter (M) of the homodyned K distribution, were estimated from ultrasonic radiofrequency (RF) signals. Sixteen patient with 22 breast cancer tumor treated with NAC sequential Anthracyclines and Taxanes were prospectively assessed. Data were acquired using 5–14 MHz array transducer, pre-chemotherapy, and four times during treatment (one week after the subsequent courses). The US results were compared with histological response analyzing the stromal changes and the cellularity of the tumor. Results: An increase in IBSC and decrease M parameters was observed in 80% of tumors with complete response after chemotherapy. It correlates with increasing the stromal elements, fibrosis, and elastosis. In patients with partial response, the parameters IBSC and M parameters did not change during subsequent cycles of treatment and allow predicting partial response in 70% of tumors. In pathological results, a similar cluster of cells without fibrosis and elastosis were observed. Conclusions: Ultrasound parameters derived from the RF data give the promise to predict the tumor response to NAC and better personalize the therapy using US QU examination. This study was supported by the National Science Centre, Poland, grant 2016/23/B/ST8/03391. No conflict of interest

attenuation estimation, parametric imaging